Pilot of New Technologies to Increase the Genomic Diagnosis of Undiagnosed Disease Network (UDN) Patients

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Abstract: Despite the diagnostic successes of the UDN program, a significant number of patients remain genetically undiagnosed after exome sequencing, genome sequencing, and transcriptome analyses. We hypothesize that longer-range data from complementary sequence analysis platforms will enhance diagnostic yield in the UDN by identifying structural variants with phasing that remain undetected using traditional short- read sequencing platforms. We propose that longer-range sequencing technologies, such as Hi-C Whole Genome Sequencing (WGS) and long-read RNAseq, will facilitate the allele-specific characterization of variants (single nucleotide, indel, and structural variants), allow transcript isoform analysis in addition to phasing of cis vs. trans variants, provide information about epigenetic and nucleomic features such as domains, loops and compartments, that will enable additional integrated analysis of all forms of noncoding variants to improve the diagnostic yield. To do this we will pilot two additional data types for UDN patient assessment: Hi-C WGS, and long-read RNAseq. This will complement established technologies in the UDN including short-read WGS and short-RNAseq, as well as add to our current investigations on long-read WGS.